Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Evolutionary Decision Support System for Stock Market Trading
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Parallel CHC algorithm for solving dynamic traveling salesman problem using many-core GPU
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
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This paper proposes a computational intelligence approach to stock market decision support systems based on a hybrid evolutionary algorithm with local search for many-core graphics processors. Trading decisions come from trading experts built on the basis of a set of specific trading rules analysing financial time series of recent stock price quotations. Constructing such trading experts is an optimization problem with a large and irregular search space that is solved by an evolutionary algorithm, based on Population-Based Incremental Learning, with additional local search. Using many-core graphics processors enables not only a reduction in the computing time, but also a combination of the optimization process with local search, which significantly improves solution qualities, without increasing the computing time. Experiments carried out on real data from the Paris Stock Exchange confirmed that the approach proposed outperforms the classic approach, in terms of the financial relevance of the investment strategies discovered as well as in terms of the computing time.